Govcon Intel

Resolve Entity

resolve_entity

Read-onlyIdempotent

"What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (cross-source identity spine: 10-digit CIK + ticker + company_name from SEC EDGAR, legal-entity LEI from GLEIF with parent/ultimate-parent/children ownership when the LEI resolves, and security FIGI from OpenFIGI — by exact ticker map when a ticker is implied, and otherwise by name search, so NON-EQUITY instruments that never have a ticker (municipal and corporate bonds, notes, authority debt) DO resolve here; when a name matches more than one instrument it asserts nothing and returns figi_candidates to pick from, which is the correct answer to an issuer name that does not identify a single bond; every identifier is labelled with the source that established it, and an identifier that could NOT be resolved is stated explicitly under unresolved rather than omitted — accepts ticker, CIK, ISIN, or company name as input; an ISIN like "CH0038863350" resolves to the LEGAL ENTITY that issued the security via the GLEIF ISIN-to-LEI mapping, covering non-US issuers EDGAR cannot reach), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/concept/{rxcui} citation; accepts brand or generic name). LEI/FIGI enrichment degrades gracefully — if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return. Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`type`	Yes	Entity type: "company" or "drug".
`value`	Yes	For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., "ozempic", "metformin"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed ("NEW YORK ST DORM AUTH"), never the question's full noun phrase ("NEW YORK ST DORM AUTH revenue bonds"): the FIGI lookup matches instrument names, so trailing security-class words match nothing.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed1 schema field changedSep 1, 2026

changedInput schema / properties / value / description

Previous value: -"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed (\"NEW YORK ST DORM AUTH\"), never the question's full noun phrase (\"NEW YORK ST DORM AUTH revenue bonds\"): the FIGI lookup matches instrument names, so trailing security-class words match nothing."

Changed3 schema fields changedMay 8, 2026

changedInput schema / properties / type / description

Previous value: -"Entity type. v1 supports \"company\"."New value: +"Entity type: \"company\" or \"drug\"."

changedInput schema / properties / type / enum

Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "drug"
+]

changedInput schema / properties / value / description

Previous value: -"Ticker, CIK, or company name (e.g., \"AAPL\", \"0000320193\", \"Apple\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."

AddedMay 8, 2026

TDQS

A4.9/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnly/openWorld/idempotent annotations, the description reveals rich behavioral details: identifiers are source-labeled, unresolved ones are explicitly listed under 'unresolved', ambiguous matches return figi_candidates rather than asserting a wrong answer, and LEI/FIGI enrichment degrades gracefully. It also explains that an ISIN resolves to the issuing legal entity via GLEIF. This far exceeds what annotations alone provide and gives the agent a clear mental model of side effects and edge cases.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-structured: opening with user-phrase examples, then diving into supported types and their nuances, and ending with fallback behavior. Every section adds necessary information, and there is no filler. The only minor deduction is that the wall of text could be slightly overwhelming, but the logical organization and front-loaded use case mitigate this.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given there is no output schema, the description takes on the burden of explaining return behavior. It mentions figi_candidates for ambiguous matches, the unresolved field, source-labeled identifiers, and the specific outputs for drugs (RxCUI + ingredient + brand). It also covers input formats, edge cases (non-US issuers, non-equity instruments), and degradation. For a tool with only two parameters and no output schema, this is remarkably complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description substantially augments the parameter docs. It provides concrete input examples ('AAPL', 'CH0038863350', 'ozempic'), warns about passing only the issuer name for bonds (avoiding trailing security-class words), and clarifies that 'value' accepts ticker, CIK, ISIN, or name for companies. This goes beyond the schema's basic descriptions and helps the agent format inputs correctly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('resolve') and resource (user-spoken names to canonical identifiers), with concrete examples of use cases. It clearly distinguishes itself from sibling tools by emphasizing that it produces identifiers other tools require as input, and explicitly names the two supported entity types (company, drug). This lets an agent understand exactly what the tool does and why it differs from alternatives like entity_profile or compare_entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly instructs 'Use FIRST whenever you have a name but need an ID,' providing a clear when-to-use condition. It also details behavior for company vs. drug types, explains how ISINs map to legal entities, and notes graceful degradation when external sources are unavailable. Although it does not name specific sibling tools to avoid, the 'FIRST' directive and the purpose of supplying inputs to other tools effectively communicate when this tool should be selected.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.1/5.0

Disambiguation3/5

Several tools have overlapping purposes (e.g., ai_visibility_check and scan_competitor_ai_presence; ask_pipeworx, ask_pipeworx_grounded, and deep_research). However, detailed descriptions and specific use cases help agents distinguish between them in most cases.

Naming Consistency5/5

All tools use snake_case naming consistently, e.g., ask_pipeworx, compare_entities, govcon_agency_landscape. The pattern is uniform across the entire set.

Tool Count4/5

With 33 tools, the set is slightly over the typical well-scoped range. While many tools serve distinct purposes, some seem redundant (e.g., memory tools, multiple research tools), making the count feel a bit heavy.

Completeness3/5

The tool surface covers a broad range of research and intelligence domains but lacks actionable tools for core government contracting tasks like submitting bids or tracking contract performance. Several obvious operations (e.g., user profile management, submission tools) are missing.

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